tbuckley/cabot-search huggingface dataset:
MIF improves immune microenvironment of Lewis lung cancer brain metastases after radiotherapy via reducing M2 macrophages.
Authors: Ying Wang, Xiaorong Dong
Journal: Journal of Clinical Oncology (2022)

e21026 Background: Our previous studies showed that inhibition of MIF/CD74 signaling pathway can promote the transformation of intracranial macrophages from M2 to M1 type after radiotherapy in brain metastases(BMs). It is reported that M2 macrophages are the main components of tumor immunosuppression microenvironment, and the surface marker, ARG-1, can decompose the Arginine which is essential in the growth and development of T cells. Therefore, this paper will focus on the mechanism of MIF regulating tumor infiltrating lymphocytes (TILs) by inducing macrophage phenotype transformation. Methods: MIF-Knockdown in Lewis cells were constructed by lentiviral shRNA interference technique. The expression levels of MIF in Lewis and iNOS (M1 type marker), Arg-1 (M2 type marker) in BV2 cells were detected by WB. The RNA expression levels of M1 and M2 tags were detected by qPCR. The expression of iNOS, Arg-1 and Iba-1 in lung cancer BMs model mice was detected by IF. The content of arginine in tumor tissue and clinical patient specimens were detected by Arginine kit. The proportion of TILs in vivo was measured by flow cytometry. BMs tumor growth in mice was observed by Small Animals Imaging Technology. Results: In animal experiments, WB、PCR and IF results showed that MIF knockdown (sh-MIF) combined with whole brain irradiation(IR) can raise the expression of iNOS, while the Arg-1 lessend, suggesting that downregulation of MIF can promote M1 phenotype and reduce M2 phenotype. Flow cytometry results showed that sh-MIF combined with IR increased the proportion of CD8+T/CD4+T cells . After clearance of macrophage, Flow cytometry showed no difference between the sh-MIF group and the control group (NC). Small Animals Imaging results also showed that the inhibition of tumor growth by sh-MIF was less significant than macrophages presence. It is indicated that down-regulation of MIF expression combined with irradiation promote T cell infiltration and inhibit tumor may be mediated by macrophages. Arginine kit results showed that the content of arginine increased after IR or sh-MIF, while it increased overall after macrophage clearance, but the difference between each groups was not significant, suggesting that IR or sh-MIF could increase the content of arginine by reducing M2-type macrophages. Flow cytometry and animals imaging results showed that the CD8+/CD4+TILs ratio was increased and tumor growth was inhibited after the addition of exogenous arginine. In clinical samples, the content of arginine in CSF decreased after radiotherapy, which suggests that arginine supplementation has the potential to improve the effect of radiotherapy. Conclusions: Inhibitory MIF expression can reduce the expression of Arg-1 from M2 type macrophages in BMs, thus raise the content of arginine, promote the infiltration of lymphocytes, improve the immune microenvironment, achieve radiotherapy sensitization finally.
The population characteristic analysis in RecurIndex for clinical decision-making on adjuvant therapy for early breast cancer: Results of a real-world study.
Authors: Xiaojia Wang, Lei Lei, Peifen Fu, Ouchen Wang, Xiaohong Xie, Haibin Xu, Weiliang Feng, Hongjian Yang, Guinv Hu, Shuguang Li, Zhijun Dai, Guilong Guo, Xiaoai Lv, Ya-bing Zheng, Zhan-hong Chen, Xi-Ying Shao
Journal: Journal of Clinical Oncology (2022)

e12514 Background: 28-gene signature (RecurIndex) is the first multi-gene panel for recurrence risk prediction after surgery developed from the Asian breast cancer population. In this study, we aimed to report the clinical and genomic characteristics of Chinese early-stage breast cancer patients whose primary lesions have been evaluated by 28-gene signature testing. Methods: In this study, 363 patients were assessed by 28-gene signatures between November 2019 and August 2021. Of these, 330 (90.9%) had stage I-II, hormone receptor-positive, HER2-negative breast cancer, 16 (4.4%) had triple-negative breast cancer (TNBC), and 17 (4.7%) had HER2-enriched breast cancer. The genomic risks of local and distant recurrence were determined by 28-gene signature respectively; the clinical risk was evaluated by the modified Adjuvant! Online. The molecular subtyping of the 28-gene signature was also compared with immunohistochemistry (IHC) status. In the future analysis, we will report the clinical decisions and outcomes of these patients. Results: The 28-gene signature classified 86.8% (n = 315/363) of patients as genomic Low Risk (LR) and 13.2% (n = 48/363) as genomic High Risk (HR) for local recurrence; 83.7% (n = 304/363) and 16.3% (n = 59/363) for distant recurrence. Among clinical HR patient, a total of 72 patients (64.9%) were deemed to be genomic LR of distant recurrence, and a total of 88 patients (79.3%) were deemed to be genomic LR of local recurrence. The concordance of luminal type identified by IHC or 28-gene based molecular subtyping was 99.1% (n = 327/330); TNBC type was 25% (n = 4/16), and HER2 enriched was 70.6% (n = 12/17). Conclusions: This study showed that in clinical HR patients, 64.9% 28-gene LR patients who could waive their adjuvant chemotherapy and 79.3% who could have the adjuvant radiotherapy adjusted. Additionally, the results of IHC and 28-gene signature-based molecular subtyping were highly consistent in luminal type, while the potential value of TNBC and HER2 enriched subtyping identified by the 28-gene signature needs to be further explored in the future.[Table: see text]
Using machine learning on real-world data to predict metastatic status.
Authors: Foad H. Green, Hu T. Huang, Michelle Lerman, Mary Tran, Vinod Subramanian, Joshua Loving, Matthew J. Rioth
Journal: Journal of Clinical Oncology (2022)

1550 Background: Real world data (RWD) is increasingly used to inform research, patient care, and population health in oncology; however, using RWD at scale requires accurate methods to identify clinically-relevant attributes. Metastatic status is a highly relevant clinical attribute in cancer patients but it is not routinely captured in structured formats and its determination conventionally requires review and interpretation by certified tumor registrars (CTRs). Clinical diagnoses, treatments, imaging procedures and other clinical variables documented in electronic health records (EHRs) can be used to differentiate metastatic from non-metastatic patients. This study describes an effective machine learning approach in utilizing prevalent and standardized data elements from EHRs across multiple health systems. Methods: 28,043 lung cancer and breast cancer patients from two large health systems within the Syapse Learning Health Network with data sources from CTR abstraction and EHRs were analyzed. Patients were labeled for reference metastatic status by CTRs and split into training (n = 22,434) and testing (n = 5,609) cohorts, with proportionate distribution of cancer type and metastatic status between cohorts. A regularized gradient boosting algorithm, XGBoost, was trained using over 750 variables from the patient records collected at the time of or after the initial cancer diagnosis. Results: Integration of ICD-10-CM codes with antineoplastic treatment history and radiologic imaging procedure orders achieved metastatic status prediction with increases to precision and recall in lung cancer (21% and 32% respectively) and breast cancer (39% and 9% respectively), when compared to the use of only ICD-10-CM diagnosis codes for secondary malignant neoplasms (Table). The addition of treatment and procedure data from different cancer types improved the model classification within individual cancer types. Conclusions: One of the biggest challenges in using RWD for precision oncology is identification of clinically-relevant phenotypes at scale. Here we demonstrate a scalable evidence-based method utilizing structured data for imputing metastatic status with high predictive power from two separate health systems. With further validation, this approach may be generalized to other cancer types, applied to temporal slices of data to identify changes in metastatic status, as well as provide a high-confidence designation of metastatic status for other use cases such as staging.[Table: see text]
Comprehensive genomic profiling of MET rare tumors in China.
Authors: Ning Li, Shuhang Wang, Qin Li, Rongrong Chen, Yuan Fang, Peiwen Ma, Ning Jiang, Yue Yu, Huilei Miao
Journal: Journal of Clinical Oncology (2022)

e15058 Background: The mesenchymal epithelial transition factor receptor (MET) is a potential therapeutic target in a number of cancers, commonly activated by amplification, mutation and fusion. Methods: Rare tumor was defined as solid tumors with an incidence lower than 2.5/100,000 per year. We retrospectively analyzed the next-generation sequencing and clinicopathological data of 3453 Chinese rare tumors patients in the Geneplus database, including 1021 genes, MSI loci, tumor mutational burden (TMB), and programmed cell death ligand-1 (PD-L1) expression analysis information. Results: MET-positive was identified in 115 patients (3.3%), including 109 cases of amplification (3.2%), 8 cases of exon 14 skipping (0.2%) and 1 cases of fusion. The prevalence of MET-positive varied widely across tumors systems and subtypes. Urinary, neural, skin and respiratory systems were the top 4 systems with 8.3% (1/12), 7.9% (58/732), 4.2% (1/24) and 2.9% (6/207) MET-positive rate, respectively. Considering the tumor subtypes, glioblastoma, sarcomatoid carcinoma, neuroendocrine tumor and glioma were higher, reaching 15.7% (16/102), 13.2% (7/53), 9.8% (4/41), and 7.4% (36/487) respectively (Table). Totally, 73.0% of patients had at least one common targetable gene mutation, including ATM，BRAF，BRCA，CDKN2A，EGFR substitution, ERBB2, FGFR1,2,3, KIT, NF1, PIK3CA, PTEN and RET. The proportions of TMB-H (≥9 mut/Mb) and MSI-H were very low, with only 8.9% and 2.2%, while PD-L1 positive rate reached to 60.9%. Conclusions: The treatment options for rare tumors are still limited. The approval of MET receptor tyrosine kinase has brought hope to patients with Non-Small Cell Lung Cancer. The MET-positive rate in rare tumors is higher than 2.7% in lung adenocarcinoma, so MET receptor tyrosine kinase have great application potential in rare tumors. However, since most patients co-occurred with other targetable driver mutations or PD-L1 positive, how to choose the best treatment strategy should be solved. The deployment of the best treatment strategy is a problem that should be solved in the follow-up study.[Table: see text]
HGG-50. Specific sensitivity of pediatric high-grade glioma with ATRX inactivation to PARP inhibitor combinations
Authors: Anna Laemmerer, Dominik Kirchhofer, Sibylle Madlener, Daniela Loetsch-Gojo, Carola N Jaunecker, Lisa Gabler, Lisa Mayr, Natalia Stepien, Alicia Baumgartner, Karin Dieckmann, Stefan M Pfister, Marcel Kool, Sonja Krausert, Apurva Gopisetty, Natalie Jäger, Andreas Peyrl, Amedeo A Azizi, Walter Berger, Johannes Gojo
Journal: Neuro-Oncology (2022)

Abstract Pediatric high-grade gliomas (pHGG) account for approximately 12% of pediatric brain tumors. Despite advances in molecular diagnosis and identification of discrete molecular subtypes, pHGG are the leading cause of cancer-related death in children. Thus, current research focuses on identifying novel therapeutic targets. Sequencing analyses across pediatric cancer types identified DNA repair perturbations as potentially targetable events in certain types of pediatric brain tumors. Herein, we investigated the potential of PARP inhibitors (PARPi), impeding the central role of PARP in DNA damage repair, in pHGG. We screened a patient-derived primary pHGG cell line panel (n=7) for their sensitivity towards 6 different PARPi (niraparib, olaparib, pamiparib, rucaparib, talazoparib, veliparib) using cell viability assays. Basal expression of DNA repair related proteins was assessed by immunoblot, and propidium iodide-based flow cytometry was used for cell cycle analysis. All pHGG were resistant towards single compound PARP inhibition. Interestingly, two H3F3A-G34R mutant pHGG models harboring inactivating ATRX mutations were characterized by elevated basal levels of pH2AX, suggesting increased stress resulting from DNA damage. Consequently, simultaneous targeting of PARP and other components of DNA repair in the respective models showed strong synergistic effects on cell viability, which was not observed to a comparable extent in other models such as BRAFV600E/TERT promotor mutant pHGG. Combination of talazoparib and irinotecan resulted in S-phase arrest. Within a precision oncology approach, we treated a 11-year-old child suffering from H3F3A-G34R mutant pHGG with ATRX mutation, that progressed during radiation, with niraparib and topotecan. The patient achieved partial remission and disease stabilization for 1 year. Taken together, PARPi combinations show potential for the treatment of pHGG with ATRX mutations. Currently, all cell models are characterized for DNA repair signatures by DNA sequencing. Further, in depth characterization of DNA damage responses upon concomitant PARP and topoisomerase inhibition in ATRX-mutated pHGG are ongoing.
Validation study of end-of-life risk prediction model in active chemotherapy patients.
Authors: Melinda S. Hill, May D. Abdo-Matkiwsky, Noa Biran, Jumana Chatiwala, Aileen L. Chen, Julianne Wilkins Childs, Denis Fitzgerald, Deena Mary Atieh Graham, Ian Horkheimer, Rachel L. Levenbach, Joseph Francis McLaughlin, Kimberly A. Salwitz, Frederick P. Smith, Robert F. Tassan, Akunna J. Ejumejowo
Journal: Journal of Clinical Oncology (2022)

e13543 Background: In February of 2020, Integra Connect (IC) released a machine learning (ML) risk prediction model called Integra Predict (IP) to predict End-of-Life (EOL) in active chemotherapy patients within the next six months. Pilot Study: Regional Cancer Care Associates (RCCA) physicians rated of likelihood of EOL in the next six months to compare concordance with the IP model predictions. Part 2 (April 2022): Patient date of death updated at the end of the six-month period will undergo post-hoc comparisons. Physician Prediction vs Actual Outcome. Integra Prediction vs Actual Outcome. Methods: A stratified random sample of 265 active chemotherapy patients from 33 RCCA providers was securely emailed via pdf documents or excel sheets to RCCA for each provider. Worksheets contained provider name, patient name, age, cancer type, and the risk of dying in the next six months (Low, Medium, High). The sample included patients from the top 10 cancer types: breast, carcinoma in situ of breast, chronic leukemia, gastro/esophageal, lung, lymphoma, multiple myeloma, pancreatic, prostate, and small intestine/colorectal cancers. The sample was comprised of patients with active chemotherapy in the care of RCCA between July 2021 through September 2021. RCCA emailed the study documentation to each provider and asked providers securely emailed risk assessments to IC. Provider responses for EOL in the next six months were scored categorically - low risk (1), medium risk (2), and high risk (3). Concordance between provider responses and IP risk predictions was determined. E.g., a provider rating of low compared to a risk model rating of high would result in a difference of 1-3 = -2. Results: Forty-two percent (14/33) providers responded to our request to rate patient specific EOL which corresponds to 40% (106/265) patients sampled. Current results have a large standard deviation (0.235) resulting in a large confidence interval (.175, .64) or (18% to 64% concordance). Ninety-five percent of the time concordance will fall in this range. Table: As displayed in Table, provider estimates matched the IP risk score estimates 41.3% of the time. The average concordance between provider risk estimate and IP risk estimate was -0.7, which is almost one risk level lower, range (-1.4, -0.3). Conclusions: Study hypothesized that risk estimates would not be 50% concordant at an alpha of 0.05. 41% are concordant with a large standard deviation of 0.235, which resulted in overlapping confidence intervals. There is no significant difference in the two risk estimates.
NOTCH4 mutation as a potential predictive biomarker for immunotherapy in NRAS wildtype melanoma.
Authors: Hongxia Li, Qin Zhang, Qianqian Duan, Yuan Tan, Tingting Sun, Chuang Qi, Yong Ren
Journal: Journal of Clinical Oncology (2022)

e21594 Background: NRAS wildtype melanoma account for 80-85% melanoma patients and have been reported had a better response for immunotherapy. Although PD-L1 expression and tumor mutation burden (TMB) have been considered as important biomarkers for immunotherapy in many types of tumors, the predictive efficacy of them in melanoma is controversial. Since evidences showed NOTCH pathway mutation was a potential predictive biomarker for immune checkpoint inhibitors in some types of tumors. Here we explored the relationship between NOTCH family genes mutations and efficacy of NRAS wildtype melanoma immunotherapy. Methods: 265 NRAS wildtype ICI-pretreatment melanomas from cBioPortal (https://www.cbioportal.org), which is divided to discovery (N = 76) and validation (N = 189) cohort to analyze the association between NOTCH family genes (NOTCH1-4) mutation and efficacy of ICIs therapy. All nonsynonymous somatic mutations, including nonsense, missense, nonstop, frameshift deletion and insertion, in-frame deletion and insertion, and splice site mutations were considered for inclusion in our study. The potential mechanism was subsequently explored through RNA expression in immunotherapy treated population. Results: Patients with NOTCH4-Mut were identified to be associated with prolonged overall survival (OS) in discovery (HR: 0.30, 95%CI: 0.11-0.83, P = 0.01) and validation cohort (HR: 0.21, 95%CI: 0.07-0.68, P = 0.003). Further exploration found that NOTCH4-Mut tumors had higher tumor mutation burden (TMB) and tumor neoantigen burden (TNB) (P &lt; 0.05). RNA data revealed that enhanced anti-tumor immunity was observed in NOTCH4-Mut tumors. Activated CD4 memory T cell, CD8 T cell, follicular helper T cell (Tfh) and Neutrophils were more abundant in NOTCH4-Mut tumors. In addition, the results of enrichment analysis showed that several immune-related pathways varied significantly between NOTCH4-Mut and NOTCH4-Wt tumors, including Interferon γ response, DNA repair, epithelial mesenchymal transition (EMT), angiogenesis. Conclusions: NOTCH4 mutation may promote tumor immunity and serve as a biomarker to predict good immune response in NRAS wildtype melanoma and guiding immunotherapeutic responsiveness.
Impact of an augmented intelligence-based tool upon the timeliness of referrals to palliative care and hospice in patients with advanced cancer in the real-world setting.
Authors: Ajeet Gajra, Yolaine Jeune-Smith, Alexandrina Balanean, Amy R. Ellis, Kelly A. Miller, John Showalter, Danielle Bergman, Sibel Blau
Journal: Journal of Clinical Oncology (2022)

12127 Background: Timely integration of palliative care (PC) and hospice management for patients with advanced cancer requires informed clinical decision-making and expectation-setting to help patients realize their end-of-life (EOL) goals. Longer stay with hospice is a quality indicator in oncology care that requires earlier referral to PC and hospice. We have previously demonstrated that an augmented intelligence (AI) tool used to predict 30-day mortality can assist with an increase in referrals to PC and hospice. In this secondary analysis, we report on the impact of the AI tool on the timeliness of referral to PC and hospice prior to death. However, calculating days with hospice across multiple hospice providers and geographies can be challenging. Thus, we used a real-world (RW) measure of 14 to 90 days prior to death as a surrogate for timeliness for hospice and 90 to 180 days prior to death for a PC referral. Methods: Medical records of patients at a large community-based hematology/oncology practice in the Pacific Northwest who experienced a mortality event pre-deployment (January 2017 to April 2018) or post-deployment (May 2018 to June 2021) of the AI tool were electronically reviewed for evidence of a PC or hospice referral. Patients were included if the referral was between 14 to 90 days of the mortality event for hospice care or between 90 to 180 days prior to death for PC. Outcomes for additional timepoints (1-3 days and 4-13 days and &gt;90 days) will be provided at the final presentation. Data were analyzed using a statistical process control chart. Results: Of the patients who experienced a mortality event, the following percentages had been referred pre- and post-deployment of the AI tool: PC 7.1% pre- and 15.0% post-deployment; hospice 11.5% pre- and 32.1% post-deployment. A system shift (≥6 points in a row steadily increasing or decreasing) occurred early after deployment, in June 2018. The overall improvements were 111.0% in PC referrals and 179.1% in hospice referrals within the respective timeframes. Conclusions: Deployment of an AI tool at a hematology/oncology practice substantially increased the proportions of patients referred to PC 90-180 days prior to death and hospice between 14 -90 days prior to death, suggesting a favorable impact on timeliness of referrals. If confirmed in additional studies, the AI-based tool can be utilized to integrate PC early in the management of patients with advanced cancer.[Table: see text]
Risankizumab as maintenance therapy for moderately to severely active Crohn's disease: results from the multicentre, randomised, double-blind, placebo-controlled, withdrawal phase 3 FORTIFY maintenance trial
Authors: Marc Ferrante, Remo Panaccione, Filip Baert, Peter Bossuyt, Jean-Frederic Colombel, Silvio Danese, Marla Dubinsky, Brian G Feagan, Tadakazu Hisamatsu, Allen Lim, James O Lindsay, Edward V Loftus, Julián Panés, Laurent Peyrin-Biroulet, Zhihua Ran, David T Rubin, William J Sandborn, Stefan Schreiber, Ezequiel Neimark, Alexandra Song, Kristina Kligys, Yinuo Pang, Valerie Pivorunas, Sofie Berg, W Rachel Duan, Bidan Huang, Jasmina Kalabic, Xiaomei Liao, Anne Robinson, Kori Wallace, Geert D'Haens
Journal: The Lancet (2022)